Fault-Tolerant and Scalable Key Management Protocol for IoT-Based Collaborative Groups

TLDR: This work proposes to enhance the previously proposed protocol (i.e. DBGK) with polynomial computations, which achieves a better scalability and fault tolerance compared to DBK and allows additional controllers to be included with no impact on storage cost regarding constrained members.

Abstract: Securing collaborative applications relies heavily on the underlying group key management protocols. Designing these protocols is challenging, especially in the context of the Internet of Things (IoT). Indeed, the presence of heterogeneous and dynamic members within the collaborative groups usually involves resource constrained entities, which require energy-aware protocols to manage frequent arrivals and departures of members. Moreover, both fault tolerance and scalability are sought for sensitive and large collaborative groups. To address these challenges, we propose to enhance our previously proposed protocol (i.e. DBGK) with polynomial computations. In fact, our contribution in this paper, allows additional controllers to be included with no impact on storage cost regarding constrained members. To assess our protocol called DsBGK, we conducted extensive simulations. Results confirmed that DsBGK achieves a better scalability and fault tolerance compared to DBGK. In addition, energy consumption induced by group key rekeying has been reduced.

Figures

Fig. 3. Storage overhead

Fig. 4. Polynomial degree

Table 1. Terminology table

Fig. 2. Network architecture

Fig. 5. Member leaving cost

Fig. 1. Use case scenario

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Fault-tolerant and Scalable Key Management Protocol
for IoT-based Collaborative Groups
Mohammed Riyadh Abdmeziem, François Charoy
To cite this version:
Mohammed Riyadh Abdmeziem, François Charoy. Fault-tolerant and Scalable Key Management
Protocol for IoT-based Collaborative Groups. SecureComm 2017 : 13th EAI International Conference
on Security and Privacy in Communication Networks, Oct 2017, Niagara falls, Canada. pp.1-20.
�hal-01588490v2�

Fault-tolerant and Scalable Key Management
Protocol for IoT-based Collaborative Groups
Mohammed Riyadh Abdmeziem
?
and François Charoy
Université de Lorraine Inria-CNRS-LORIA,
Nancy, France
{mohammed-riyadh.abdmeziem,francois.charoy}@loria.fr
Abstract.
Securing collaborative applications relies heavily on the un-
derlying group key management protocols. Designing these protocols is
challenging, especially in the context of the Internet of Things (IoT).
Indeed, the presence of heterogeneous and dynamic members within
the collaborative groups usually involves resource constrained entities,
which require energy-aware protocols to manage frequent arrivals and
departures of members. Moreover, both fault tolerance and scalabil-
ity are sought for sensitive and large collaborative groups. To address
these challenges, we propose to enhance our previously proposed proto-
col (i.e. DBGK) with polynomial computations. In fact, our contribution
in this paper, allows additional controllers to be included with no impact
on storage cost regarding constrained members. To assess our protocol
called DsBGK, we conducted extensive simulations. Results conrmed
that DsBGK achieves a better scalability and fault tolerance compared
to DBGK. In addition, energy consumption induced by group key rekey-
ing has been reduced.
Key words:
Collaborative applications, Internet of Things (IoT), Se-
curity, Group key management, Polynomial computation, Contiki.
1 Introduction
With the rise of the Internet of Things (IoT) and its integration in informa-
tion systems, collaborative applications have taken a new dimension. Pervasive
devices and objects are able to perceive our direct environment and act au-
tonomously upon it to help users to reach their goals. Applications ourished
in healthcare, transportation and military environments [4] that combine input
from users and objects to reach goals in a collaborative way. In these domains,
stakeholders would only accept these systems in their environment if they have
strong guarantees on the security, privacy and integrity of the data they pro-
duce and share. The distributed nature of such systems and the requirement for
encryption of data shared among participants lead to one of the most impor-
tant challenges in such evolving environments: the management of cryptographic
group keys [32] [6] [2].
?
Corresponding author

2 M.R. Abdmeziem and F. Charoy
Group key management is challenging in this context. In fact, collaborative
groups involve heterogeneous members with dierent requirements and resources
capabilities [17]. This gap can hinder end-to-end communications. Indeed, con-
strained members with limited processing power and storage space can not run
heavy cryptographic primitives [5]. Moreover, collaborative applications may
present a high rate of leaving and joining members within tight time lapses,
which makes the issue more dicult to handle. The scalability of these systems
needs to be addressed bearing in mind the increasing number of entities taking
part in the collaborative groups. Last, fault tolerance is at utmost importance
especially for critical and sensitive applications (e.g. health related and military
applications) [31].
We address this problematic of designing a secure and ecient protocol to
establish shared group credentials for Peer-to Peer collaborative groups. These
credentials will be used to ensure the required security properties such as data
condentiality, data integrity, and data authentication. The proposed protocol
has to be energy aware allowing an implementation on constrained devices, which
take part in the collaborative process. In addition, the protocol must be scalable,
as well as tolerant to possible failures of the entity in charge of managing the
group key.
To achieve this goal, we rely on our previously proposed group key manage-
ment protocol called DBGK (Decentralized Batch-based Group Key) [3]. This
protocol considers a network topology composed of several sub groups. Each sub
group is managed by an area key management server, while the whole group
is managed by a general group key management server. The established group
key is composed of a long term key and short terms keys (called tickets), which
are dierent for each time interval. Constrained members in terms of resources
(e.g. connected objects) are only involved in the re-keying process if these latter
have recently been active. In addition, keying materials are distributed to joining
members based on their resources capabilities. Experiments showed that DBGK
[3] is energy ecient and outperforms similar existing protocols in the literature.
Although ecient and secure, DBGK relies on key management servers to
maintain the group key. Including additional servers to improve fault tolerance
would impose a high storage overhead on constrained members. This makes
DBGK inappropriate to be directly implemented in sensitive collaborative ap-
plications. In this paper, we propose a distributed extension for DBGK called
DsBGK (Distributed Batch-based Group Key). In this extension, we keep the
core functioning of DBGK, while signicantly distributing the operations which
were based on a central entity. We achieve this by integrating a polynomial
based scheme inspired from [25] and [24]. In addition, we improve the eciency
of the original scheme to suit the constrained IoT environment. We conducted
extensive experiments to assess the performances of DsBGK and compared the
results with DBGK performances. The results showed that DsBGK provides an
enhanced scalability and fault tolerance, as additional key management servers
(controllers) can be included without impacting the storage overhead on con-
strained members. Furthermore, energy cost due to rekeying operations is re-

Fault-tolerant and Scalable Key Management Protocol (DsBGK) 3
duced compared to DBGK, which extends the life cycle of battery powered
entities.
The remaining of the paper is organized as follows. In section 2, we present
a use case scenario to motivate our contribution. In section 3, we discuss, in
detail, existing solutions in the literature. For the sake of clarity, we summarize
in section 4, the required background. In section 5, we present our network model,
along with our assumptions and the used notations. In section 6, we thoroughly
present our approach before introducing and analyzing the experimental results
in section 7. Section 8 concludes the paper and sets our future direction.
2 Use case scenario: Personal Health Record (PHR)
Internet
Internet
shared medical record
Medical team edit the medical
record using smart phone/PC
Patient’s physiological data is
captured through sensors
Fig. 1.
Use case scenario
A personal heath record [33] (Fig. 1) is a typical example of a document that
can be accessed and edited by multiple participants, including medical sensors
attached to patients. This is also an example of a document that contains highly
private and sensitive information. To edit a medical record, some participants
(e.g. medical sta) collaborate using unconstrained devices, such as Personal
Computers (PC) and smartphones. However, sensors planted in or around the
human body are considered as constrained since they have limited computing
power and may operate on battery. These sensors can either communicate their
sensed data to medical sta through the unconstrained entities (e.g. PC, smart-
phones) or directly edit patient's medical record. Medical sta can also control

4 M.R. Abdmeziem and F. Charoy
the sensors (trigger or stop the sensing of a particular physiological data), and
add more sensors to the collaboration. New members can join or leave the col-
laboration around the medical record as the situation of the patient evolves.
The dierent entities collaborate in a distributed way to maintain the medical
record. This latter can be replicated among dierent entities and the modica-
tions can be executed on the dierent replicas, which need to be synchronized.
This is important in order to avoid a single point of failure on the record man-
agement architecture. It is also important to control the entities that have access
and can modify the record over time. This clearly highlights the importance of
securing communications in such a hybrid and heterogeneous group of entities
by eciently managing the security credentials used to provide data authenti-
cation and data condentiality. Personal Health Record (PHR) is a typical case
of collaboration among health-care personal, insurers, caregivers, patients and
sensors to maintain a document that reects the patient status, health history
and treatment. There is an obvious need to provide a decentralized, secure, safe,
privacy preserving and scalable solution to share these documents among people
and sensors (objects).
3 Related work
In this section, we review the main categories under which group key manage-
ment protocols are usually categorized [11] [28], namely, the centralized, the
decentralized, and the distributed categories.
Centralized
protocols are based on an unconstrained central entity (i.e. Key
Management Server (KMS)), which is responsible for generating, distributing,
and updating the group key for the whole group. Authors in [15] introduced
the Group Key Management Protocol (GKMP), which is based on a Group
Key Packet (GKP). This latter encompasses a Group Trac Encryption Key
(GTEK) to secure data trac, and a Group Key Encryption Key (GKEK) to
secure transmissions related to rekeying operations. Following a leave event, the
central entity broadcasts the new GKP to all remaining members creating a
complexity of
O(n)
. This complexity makes GKMP not scalable with regards
to dynamic and large groups. To reduce the impact of leave events, authors in
[34] proposed an interval-based protocol, which generates the keying materials
corresponding to the predicted period of time during which the members are
expected to remain in the group. Doing so, following a leave event, no rekeying is
required. However, this solution is not suited to dynamic groups with unexpected
join and leave events, as predicting the leaving moment of members is neither
realistic nor practical. In addition, constrained members which are part of the
group for a long period of time might suer from storage issues, as a large number
of keying materials needs to be stored.
To further improve eciency, several hierarchical based protocols have been
proposed. Among them, the Logical Key Hierarchy (LKH) protocol [37], later
improved by the One-way Function Tree protocol [7] are typical examples. The

Frequently asked questions

Q1. What have the authors contributed in "Fault-tolerant and scalable key management protocol for iot-based collaborative groups" ?

To address these challenges, the authors propose to enhance their previously proposed protocol ( i. e. DBGK ) with polynomial computations. In fact, their contribution in this paper, allows additional controllers to be included with no impact on storage cost regarding constrained members. To assess their protocol called DsBGK, the authors conducted extensive simulations. 

Q2. What future works have the authors mentioned in the paper "Fault-tolerant and scalable key management protocol for iot-based collaborative groups" ?

The authors plan to further investigate DsBGK security strength by thoroughly assessing properties such as data integrity, data authentication, and data con dentiality through an implementation using automated formal validation tools ( e. g. Avispa, Scyther ). 

Q3. What is the code for generating the univariate polynomial?

To secure the transmission of tickets, the active controller generates a univariate polynomial P (x) modulo the product of two large prime numbers. 

Q4. What is the cost of asymmetric primitives?

distributed protocolsinvolve a high number of exchanged messages during rekeying operations, in addition to an important computation cost due to the use of heavy asymmetric primitives. 

Q5. What is the simplest way to authenticate an object?

In case of a successful authentication, the object is initialized (through a secure channel) with a long term key (i.e. SK), and a shared key with its AKMS. 

Q6. What are some examples of hierarchical based protocols?

Among them, the Logical Key Hierarchy (LKH) protocol [37], later improved by the One-way Function Tree protocol [7] are typical examples. 

Q7. How many members are in possession of the same tickets?

Based on experimental results (see section IV.B in [3]), DBGK outperforms its peers within a proportion of around 50% of the members in possession of the same tickets as the leaving (ejected) member. 

Q8. What is the drawback of the DH primitive?

Compared to other solutions based on DH primitives, one of the drawbacks of this protocol lies in the pre-sharing assumption of the seeds, which a ects both its scalability and feasibility. 

Q9. What are the main categories of group key management protocols?

In this section, the authors review the main categories under which group key management protocols are usually categorized [11] [28], namely, the centralized, the decentralized, and the distributed categories. 

Q10. How do the authors set the degree of the polynomial?

the authors set the degree m of the polynomial in a way to keep the factorization not easily feasible while maintaining e ciency.